Gennaro Nicola Bifulco

Learn More
DRIVE IN<sup>2</sup> is an automotive research project within the field of Intelligent Transportation Systems, especially Advanced Driving Assistance Systems (ADAS). The project originates from the idea that the development of new ADAS and evaluation of their effect have to take drivers into account, as well as their behavior while driving: the benefits of(More)
Reduction of the environmental impact of cars represents one of the biggest transport industry challenges. Beyond more efficient engines, a promising approach is to use eco-driving technologies that help drivers achieve lower fuel consumption and emission levels. In this study, a real-time microscopic fuel consumption model was developed. It was designed to(More)
Identification of driving behavior is a crucial task in several Intelligent Transportation Systems applications, both to increase safety and assist drivers. Here we identify driving behaviors by means of an analytical model. In order to estimate the model parameters, data are collected with an instrumented vehicle. The paper presents the model, the(More)
The investigation of the effects of information on travellers' behaviour at individual level is a pre-requisite for any analysis on the impacts of ATIS on traffic networks. It is widely expected that travellers' behaviour can be strongly influenced by the ability of the information system in making accurate estimations of the actual travel times they will(More)
This paper analyses driving behaviour in car-following conditions, based on extensive individual vehicle data collected during experimental field surveys carried out in Italy and the UK. The aim is to contribute to identify simple evidence to be exploited in the ongoing process of driving assistance and automation which, in turn, would reduce rear-end(More)
Modelling car-following in an effective and accurate way is of great importance for several areas of application, such as microscopic traffic simulation and ADAS (Advanced Driving Assistance Systems). Heterogeneity can be observed in driving behaviors if car-following data are analyzed. Part of this dispersion depends on the inherent heterogeneity across(More)
This paper analyses the behaviour of travellers' in a choice context in which they are assisted by ATIS (Advance Traveller Information Systems). In order to observe travellers' response to information, stated preference approaches are often adopted for data collection. To this aim, at the Department of Transportation Engineering of the University of Naples,(More)
  • 1